code: def _make_sparse_dataset(csr_container, float_dtype):
      if float_dtype == np.float32:
          X, y, sample_weight, csr_dataset = (
              X32,
              y32,
              sample_weight32,
              CSRDataset32,
          )
      else:
          X, y, sample_weight, csr_dataset = (
              X64,
              y64,
              sample_weight64,
              CSRDataset64,
          )
      X = csr_container(X)
      return csr_dataset(
          X.data, X.indptr, X.indices, y, sample_weight, seed=42
      )
  
file_path: scikit-learn/sklearn/utils/tests/test_seq_dataset.py
imports: from itertools import product
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.datasets import load_iris
  from sklearn.utils._seq_dataset import ArrayDataset32, ArrayDataset64, CSRDataset32, CSRDataset64
  from sklearn.utils._testing import assert_allclose
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 3 for calling a global function with example usage: _make_sparse_dataset(csr_container, float_dtype) and returns: csr_dataset